Wikimedia Commons SDC Scraper

Wikimedia Commons SDC Scraper

Scrape structured data from any Wikimedia Commons media file, up to a million entities per run. Each file returns its SDC statements, property-value pairs, qualifiers, and references as a flat row. No login or API key. Export to CSV, JSON, Excel, or XML.

Wikimedia Commons hosts millions of media files, each with structured data (SDC) that describes the file's content, creator, license, and more. The official API requires complex queries and doesn't expose SDC directly. This actor reads the SDC from file pages and returns each entity's data in a consistent schema.

Who uses it	What they scrape Wikimedia Commons SDC for
Digital asset managers	To audit metadata completeness across a library of Commons files.
Data scientists	To build datasets of image metadata for training or analysis.
GLAM institutions	To extract rights and provenance data from their contributed media.
Wikimedians	To monitor SDC usage or find files missing key statements.

What it does

This actor collects structured data (SDC) from Wikimedia Commons media files by entity ID, and returns each file's property-value pairs, qualifiers, and references as a flat row.

• Structured data extraction: Each file returns all SDC statements, including property IDs, values, qualifiers, and references.
• Bulk by entity ID: Input a comma-separated list of entity IDs (e.g. M123456) to scrape them all in one run.
• Flat output: Every entity becomes one row with columns for each property, so you can filter and analyze in any tool.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with Wikimedia Commons SDC data

Audit metadata completeness.

A GLAM institution runs the actor on its contributed files to find which ones lack required SDC statements like creator or license.

Build a research dataset.

A data scientist collects SDC from thousands of images to study how Commons contributors describe cultural heritage objects.

Monitor SDC adoption.

A Wikimedia project manager tracks how many files in a category have SDC statements, using the actor to sample and count.

Migrate metadata.

A digital library extracts SDC from Commons files to map the data into its own metadata schema for reuse.

Why choose this scraper

	What you get
Property IDs	The SDC property (e.g. P180 for depicts) for each statement.
Values	The value of each property, which can be a string, URL, or another entity ID.
Qualifiers	Additional context for a statement, like precision or source.
References	The source or evidence for each statement, if provided.
Flat schema	All data in one row per file, ready for spreadsheets or databases.

What a Wikimedia Commons SDC record looks like

Every record returns as one flat JSON row. Here is a real one from a run:

{
 "imageUrl": "https://commons.wikimedia.org/wiki/M123456",
 "entity_id": "M123456",
 "entity_type": "mediainfo",
 "media_type": "image/png",
 "sha1_hash": "3fedc6d4d11a97d49558cfa12d55f916bbf9215f",
 "sha1_determination_method": "Q13414952",
 "data_size_bytes": 6197,
 "data_size_unit": "byte",
 "height_pixels": 169,
 "height_unit": "pixel",
 "width_pixels": 62,
 "width_unit": "pixel",
 "scrapedAt": "2026-08-26T17:53:12.705Z"
}

Every value above comes from a real run. A field a record does not have comes back as null.

Configure the run

Drive the actor from a list of Wikimedia Commons entity IDs. Each ID corresponds to a media file; the actor reads the SDC from its page and returns only the structured data statements. The Input tab lists every parameter.

A first run with the defaults:

{
 "entityIds": "M123456",
 "maxItems": 10
}

A larger pull:

{
 "entityIds": "M123456",
 "maxItems": 200
}

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

1. Create a free Apify account.
2. Open the Wikimedia Commons SDC Scraper.
3. Set your inputs and any filters, then click Start.
4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to Wikimedia Commons SDC through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

$claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/wikimedia-commons-sdc-scraper"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

Why am I getting no results?

Check that your entity IDs are valid and start with 'M' followed by digits. Also ensure the files exist on Commons and are not deleted or restricted.

The actor returns empty data for some files.

Some files may have no structured data (SDC) at all. The actor will still list the file but with empty SDC fields. Verify the file page has SDC statements.

My run is taking too long.

Large lists of entity IDs can take time. The actor processes each file individually. Consider reducing the number of IDs or upgrading your Apify plan for faster runs.

I see an error about invalid entity ID.

Entity IDs must be in the format M followed by digits (e.g. M123456). Remove any spaces or extra characters from your list.

The output has too many columns.

The flat schema creates a column for each unique property found across all files. If you need a simpler output, filter the dataset after export.

FAQ

Question	Answer
What is an entity ID on Wikimedia Commons?	An entity ID is a code like M123456 that identifies a media file in the Commons database. You can find it in the file's URL or its page source.
How do I find entity IDs for the files I want?	You can get entity IDs from Commons category pages, search results, or by using the Commons API to list files. The actor expects a comma-separated list of these IDs.
What data does the actor return?	It returns the structured data statements for each file, including property IDs, values, qualifiers, and references. The exact fields depend on what SDC is present on the file page.
Can I scrape all files in a category?	The actor currently works from a list of entity IDs. To scrape a category, you would first get the file list from Commons and then pass the IDs to this actor.
Is there a limit on how many files I can scrape?	Free users are limited to 10 items for preview. Paid users can set a max up to 1,000,000 entities per run.
Do I need a Wikimedia account or API key?	No. The actor reads public file pages directly. No login or API key is required.
What export formats are available?	You can export the results as CSV, JSON, Excel, or XML from the Apify dataset.
How does the actor handle files with no SDC?	Files without any structured data will still appear in the results, but the SDC columns will be empty or null.
Can I scrape files from other Wikimedia projects like Wikipedia?	No. This actor is designed specifically for Wikimedia Commons SDC. Other projects use different data structures.
What if a file has multiple statements for the same property?	The actor returns all statements. In the flat output, multiple values for the same property may appear in separate rows or concatenated, depending on the schema.

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Browse the full ParseForge collection for more scrapers.

🆘 Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Wikimedia Foundation, Inc. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.

💰 How much does it cost to scrape Wikimedia Commons SDC?

This Actor uses pay-per-result pricing: $0.004 per result collected. You are billed only for the results you receive, so a run that returns nothing costs nothing.